基于Landsat-5TM的洪河湿地地表温度估算方法对比研究

利用洪河湿地2008年5月15日过境的Landsat／TM图像和实测地面数据以及MODIS地表发射率数据,分别运用大气辐射传输模型、覃志豪的单窗算法和Jimenez—Munoz ＆ Sobrino的单波段算法估算洪河湿地的地表温度,并且对比了大气校正前后的NDVI、LSE以及各种算法估算地表温度的差异。分析估算结果表明,覃志豪的单窗算法与实测地面数据估算结果非常一致。指出在没有实时探空数据的情况下,应用只有一个热红外通道的Landsat／TM数据源,采用覃志豪的单窗算法估算的精度是可以接受的。     

基于Landsat-5 TM 估算地表温度

基于Landsat25 TM 的热红外波段(6 波段) 数据, 运用覃志豪的单窗算法估算了张掖绿洲地区的地表温度, 结果表明沙漠和戈壁的地表温度最高, 水体和绿洲的温度最低, 估算的地表温度的结果符合地表水热关系。因此, 这种方法能较好地反演出张掖绿洲地表温度的分布状况。     

TM热红外波段等效比辐射率估算

地表比辐射率是热红外遥感获取地表温度必不可少的参数。目前,实验室或野外实际测量的都是8~14um热红外波段范围内的地表比辐射率,这与Landsat 5 TM热红外波段10.4~12.5um范围内的地表比辐射率还存在着一定的差异。本文将着重探讨TM热红外范围内地表比辐射率的估算方法,然后根据估算出的地表比辐射率,利用覃志豪等提出的单窗算法[1~2],对北京城八区进行地表温度反演。结果表明,该方法能获得较为合理的地表温度反演结果。     

高寒山区地表温度反演算法对比——以疏勒河上游流域为例

针对高寒山区地表温度遥感反演误差较大的问题,对比了三种地表温度算法在疏勒河上游流域的适用性。利用2009~2011年9景Landsat-5TM影像和气象数据,对疏勒河上游高寒山区的地表温度进行了反演。地表实测数据与三种地表温度算法及三种比辐射率计算方案下的反演结果进行对比检验的结果表明:辐射传输方程和普适性单通道算法的反演结果均高于实测值,单窗算法的误差最小,采用单窗地表温度算法结合覃志豪等的比辐射率计算方案反演的地表温度与实测结果的一致性最好。对2010年6月9日的不同下垫面类型的地表温度的空间分布分析结果表明,优化组合的地表温度算法反演的地表温度能够反映疏勒河上游山区不同地物的地表温度差别。     

喀斯特城市地表温度遥感反演算法比较

针对喀斯特城市快速扩展所引发的热环境问题,提出喀斯特山峰混合像元比辐射率估算方法,使Landsat 8遥感数据的地表温度反演算法适用于喀斯特城市,利用5种单通道算法和劈窗算法反演地表温度,分析反演精度和敏感性因子。结果表明:在我国南方喀斯特地区大气水分含量较高的情况下,单通道算法比劈窗算法精度更高,Jimenez单通道算法(JSC)和覃志豪单窗算法(QMW)更适用于喀斯特城市地表温度反演,反演值和实测值的误差在1.0℃内。反演地表温度的统计值以JSC算法与QMW算法相近,平均值的差值为0.26℃,标准差的差值为0.01℃,建筑和裸岩温度平均值的差值分别为0.43℃和0.54℃,高于水体和茂密植被;Jimenez劈窗算法与Rozenstein劈窗算法相近,平均值的差值为1.14℃,标准差的差值为0.19℃;Weng单通道算法在劈窗算法与JSC和QMW算法之间。各算法对比辐射率ε较敏感,ε每增加0.01,地表温度反演值误差增加0.4～0.7℃;除QMW算法反演值随近地面气温每增加1.0℃而引入近0.5℃误差外,各算法对近地面气温、大气总水分含量、大气透射率的敏感性相对较低。研究结果可为喀斯特城市热环境监测提供科学依据。     

基于TM6的城市居民建筑节能研究

基于2009年北京西城区1月份的TM影像,利用覃志豪的单窗算法对西城区地表温度进行了反演,并通过SPOT5影像提取的居民小区分布图获取了西城区居民小区温度分布图。然后,基于热红外成像仪实测居民小区温度对居民小区温度TM6反演结果进行了验证。结果表明:居民小区的反演温度与红外热像仪实测温度具有高度的相关性,R2可达0.8416,基于TM6反演的居民小区温度信息可以有效地反映居民小区建筑热散失差异。通过与实地调查居民小区建筑及供暖信息相结合,可对居民小区的建筑节能进行科学地评价。
     

基于Landsat8的地表温度反演算法研究——以滇池流域为例

2013年2月11日Landsat 8在加州范德堡空军基地发射升空,其携带的热红外传感器为反演地表温度提供了一种新的数据,但目前尚没有针对Landsat 8热红外波段反演地表温度的算法。针对Landsat 8第10波段特征,对现有反演地表温度的单窗算法进行了参数修正,得到了用Landsat 8第10波段反演地表温度的单窗算法系数。为了评价修正后算法的精度,用MODTRAN模拟地表温度为20、30和40℃时大气水汽含量分别为1.0、1.5、2.0和2.5g·cm-2传感器高度处的热辐射值,再将模拟数据用修正后的单窗算法反演地表温度,结果表明:地表温度越低、大气水汽含量越低,误差越小;模拟结果的平均误差为0.74℃。说明基于Landsat 8第10波段用修正后的单窗算法反演地表温度是可行的,该方法可为地表温度反演提供一种途径。最后以滇池流域为例,基于2013年4月20日的Landsat 8热红外数据反演了滇池流域的地表温度,并分析了滇池流域地表温度的分布特征。     

使用单窗算法研究北京城区热岛效应

随着全球变暖和城市化进程的加快,大城市城区的热岛效应日益严重。城市下垫面对地表能量交换的影响巨大,引起地表温度分布的不均一性。遥感技术的发展为地表温度的反演提供了可能。近年来人们使用劈窗算法对均一的海面温度的反演很成功,但是受空间分辨率的限制以及陆面的不均一性,陆面温度的反演一直是一个没有解决好的问题。覃志豪提出了一种TM热红外波段单窗算法,可以利用辅助气象资料快速计算出地表温度。本文以北京市城区为研究区,采用LandsetETM第6波段的单窗算法,反演了亮度温度和地表实际温度,分析了城市下垫面情况下NDVI与地表温度的相关关系,并解释了北京城区热岛在空间上的分布及其可能的原因。结果表明:北京市城区热岛效应显著;地表温度与NDVI相关性显著;城区绿地和水体在区域的温度分布中起到重要作用。     

基于Landsat系列数据地表温度反演算法对比分析——以齐齐哈尔市辖区为例

Landsat热红外系列数据是地表温度反演的一项重要数据源。以齐齐哈尔市辖区为研究区域,基于2002、2008和2016年Landsat TM/ETM+/TIRS系列数据,分别采用单窗算法(MW算法)、单通道算法(SC算法)和辐射传输方程法(RTE算法)进行地表温度反演及对比分析,并利用MODIS地表温度产品对反演结果进行精度验证。结果表明:(1)基于Landsat系列数据,3种算法反演得到的地表温度的空间分布状况一致,总体上市区地表温度较高,水体区域温度最低;(2)基于ETM+数据,SC和RTE算法结果一致性较好,其中SC算法精度最高,MW算法在不同地物覆被区误差均较大;(3)MW算法基于TM数据反演精度最高,RTE算法次之,SC算法较差;(4)基于Landsat 8TIRS数据,SC算法精度最高,RTE算法误差较大。     

基于MODIS数据的湖面温度反演算法精度对比研究——以太湖为例

以MODIS(Terra)影像数据为数据源,对比了不同分裂窗算法反演2012年太湖湖泊表面温度结果,并通过太湖水环境自动监测站网实测数据与不同算法结果进行了精度对比分析.结果表明,MODIS地表温度产品(Version 5)和覃志豪算法产品所反演的太湖表面温度精度都很高,其与实测数据的均方根误差分别为1.189℃和0.987℃.在综合数据获取、处理和适用性的基础上,研究认为,在水文、气象和生态等科学研究中可以直接利用MODIS地表温度产品(Version 5)来获取太湖地区的湖泊表面水温.     

Land surface temperature retrieval from LANDSAT TM 5

In this paper, three methods to retrieve the land surface temperature (LST) from thermal infrared data supplied by band 6 of the Thematic Mapper (TM) sensor onboard the Landsat 5 satellite are compared. The first of them lies on the estimation of the land surface temperature from the radiative transfer equation using in situ radiosounding data. The others two are the mono-window algorithm developed by Qin et al. [International Journal of Remote Sensing 22 (2001) 3719] and the single-channel algorithm developed by Jiménez-Muñoz and Sobrino [Journal of Geophysical Research 108 (2003)]. The land surface emissivity (LSE) values needed in order to apply these methods have been estimated from a methodology that uses the visible and near infrared bands. Finally, we present a comparison between the LST measured in situ and the retrieved by the algorithms over an agricultural region of Spain (La Plana de Requena-Utiel). The results show a root mean square deviation (rmsd) of 0.009 for emissivity and lower than 1 K for land surface temperature when the Jiménez-Muñoz algorithm is used.     

A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region

Remote sensing of land surface temperature (LST) from the thermal band data of Landsat Thematic Mapper (TM) still remains unused in comparison with the extensive studies of its visible and near-infrared (NIR) bands for various applications. The brightness temperature can be computed from the digital number (DN) of TM6 data using the equation provided by the National Aeronautics and Space Administration (NASA). However, a proper algorithm for retrieving LST from the only one thermal band of the sensor still remains unavailable due to many difficulties in the atmospheric correction. Based on thermal radiance transfer equation, an attempt has been made in the paper to develop a mono-window algorithm for retrieving LST from Landsat TM6 data. Three parameters are required for the algorithm: emissivity, transmittance and effective mean atmospheric temperature. Method about determination of atmospheric transmittance is given in the paper through the simulation of atmospheric conditions with LOWTRAN 7 program. A practicable approach of estimating effective mean atmospheric temperature from local meteorological observation is also proposed in the paper when the in situ atmospheric profile data is unavailable at the satellite pass, which is generally the case in the real world especially for the images in the past. Sensitivity analysis of the algorithm indicates that the possible error of ground emissivity, which is difficult to estimate, has relatively insignificant impact on the probable LST estimation error i T, which is sensible to the possible error of transmittance i 6 and mean atmospheric temperature i T a . Validation of the simulated data for various situations of seven typical atmospheres indicates that the algorithm is able to provide an accurate LST retrieval from TM6 data. The LST difference between the retrieved and the simulated ones is less than 0.4°C for most situations. Application of the algorithm to the sand dunes across the Israel-Egypt border results in a reasonable LST estimation of the region. Based on this LST estimation, spatial variation of the interesting thermal phenomenon has been analysed for comparison of LST difference across the border. The result shows that the Israeli side does have significantly higher surface temperature in spite of its denser vegetation cover than the Egyptian side where bare sand is prevalent.     

A C++ program for retrieving land surface temperature from the data of Landsat TM/ETM+ band6

A C++ language-based software tool for retrieving land surface temperature (LST) from the data of Landsat TM/ETM+ band6 is developed. It has two main functional modules: (1) Three methods to compute the ground emissivity based on land use/cover classification image, NDVI image and the ratio values of vegetation and bare ground and (2) Converting digital numbers (DNs) from TM/ETM+ band6 to LST. In the software tool, Qin et al.'s mono-window algorithm and Jiménez-Muňoz and Sobrino's single channel algorithm are programmed to retrieve LST. It will be a useful software tool to study the thermal environment of ground surface or the energy balance between the ground and the bottom atmosphere by using the thermal band of Landsat TM/ETM+.     

Improvements in land surface temperature retrieval based on atmospheric water vapour content and atmospheric temperature

Three methods are currently used to retrieve land surface temperatures (LSTs) from thermal infrared data supplied by the Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors: the radiative transfer equation, mono-window, and generalized single-channel algorithms. Most retrieval results obtained using these three methods have an average error of more than 1 K. But if the regional mean atmospheric water vapour content and temperature are supplied by in situ radiosounding observations, the mono-window algorithm is able to provide better results, with a mean error of 0.5 K. However, there are no in situ radiosounding data for most regions. This article provides an improved method to retrieve LST from Landsat TM and ETM+ data using atmospheric water vapour content and atmospheric temperature, which can be obtained from remote-sensing data. The atmospheric water vapour content at the pixel scale was first calculated from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The emissivities of various land covers and uses were then defined by Landsat TM or ETM+ data. In addition, the temperature–vegetation index method was applied to map area-wide instantaneous near-surface air temperatures. The parameters of mean atmospheric water vapour content and temperature and land surface emissivity were finally inputted to the mono-window algorithm to improve the LST retrieval precision. Our results indicate that this improved mono-window algorithm gave a significantly better retrieval of the estimated LST than that using the standard mono-window algorithm, not only in dry and elevated mountain regions but also in humid regions, as shown by the bias, standard deviation (σ), and root mean square deviation (RMSD). In Madoi County, the improved mono-window algorithm validated against the LST values measured in situ produced a bias and RMSD of –0.63 K and 0.91 K, respectively, compared with the mono-window algorithm’s bias and RMSD of –1.08 K and 1.27 K. Validated against the radiance-based method, the improved algorithm shows bias and RMSD values of –1.08 K and 1.27 K, respectively, compared with the initial algorithm’s bias and RMSD –1.65 K and 1.75 K. Additionally, the improved mono-window algorithm also appeared to be more accurate than the mono-window algorithm, with lower error values when validated against in situ measurement and the radiance-based method in the validation area in Zhangye City, Gansu Province, China. Remarkable LST accuracy improvements are shown by the improved mono-window algorithm, with better agreement not only with the in situ measurements but also with the simulated LSTs in the two validation areas, indicating the soundness and suitability of this method.     

利用TM6数据反演陆地表面温度新算法研究

陆地表面温度(LST)反演一直是热红外遥感研究中的一大难题。虽然TM 6数据具有较高的空间分辨率(120 m),但由于只有一个热通道,要得到地表真实温度,原来需要利用辐射传输方程的方法,实时资料的缺乏限制了该方法的应用。因而由TM 6数据得到的通常都是星上亮度温度,而星上亮度温度与实际地表温度差距较大,因此,其反演的温度精度不高。而单窗算法和普适性单通道算法的提出为从TM 6数据较高精度地反演陆地表面温度提供了可能。分析和研究了这两个新的单通道温度反演算法,并针对北京市的实际情况,利用2005年5月6日的TM数据对北京市的陆地表面温度进行了反演,并用实地测量数据进行了比较验证。结果表明这两种温度反演算法都取得了较高的精度,它们的rm sd值分别为1.38°和2.18°。     

基于Landsat-5 TM数据的河南省白沙灌区地表温度反演研究

地表温度是土壤水分和植被水分状态的指示计,在干旱遥感监测中有重要作用。应用Landsat-5 TM遥感数据和气象资料,利用归一化植被指数(NDVI)区分地表覆盖类型,采用Van de Griend的经验公式法结合典型地表赋值法计算出地表比辐射率。用单窗算法和单通道算法分别对河南省白沙灌区地表温度进行反演,结果表明:两种方法均能较好地将白沙灌区地表温度分布趋势反映出来,单窗算法的反演精度较高,绝对误差为1.1 ℃,更适宜白沙灌区的地表温度反演,进而可以提高灌区旱情遥感监测精度。     

基于Landsat TM图像的北京城市地表温度遥感反演研究

利用北京地区Landsat TM热红外波段数据，采用单通道算法反演得到北京地区地面温度分布图。从反演结果可以看出，北京城区地面温度比郊区地表温度高，郊区地表温度较低，密云水库、官厅水库等水体的温度最低，总体上北京城市热岛效应显著。地表比辐射率是通过Van经验公式反演得到，通过对比分析，表明该方法对自然地表的比辐射率反演效果较好。